Principal Product Manager – AI at AECOM2

As a Principal Product Manager – AI, you will lead the vision, strategy, and execution of AI products across AECOM. This role ensures AI solutions deliver measurable business value, align with organizational priorities, and scale across the enterprise. AI is a transformative force for improving efficiency, innovation, and knowledge access across AECOM. Without strong product leadership, AI initiatives risk duplication, fragmented delivery, and misaligned roadmaps. This role ensures AI capabilities evolve as a cohesive portfolio by prioritizing reusable solutions, aligning delivery to strategic goals, and maintaining consistent standards for product quality and outcomes. One of the key products supported by this role is AECOM’s enterprise AI assistant platform, an internally developed conversational AI capability used by more than 55,000 employees globally. The platform enables employees to interact with internal knowledge, documentation, and resources through natural language to quickly discover information, summarize materials, and streamline everyday work. This is a hands-on individual contributor role responsible for defining product capabilities, guiding development of LLM-powered features, and ensuring AI products deliver strong adoption, reliability, and business impact. This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work, to be based from either Houston or Dallas, TX. Key Responsibilities • Lead the product strategy and roadmap for the organization’s enterprise AI platform, ensuring it evolves as a scalable capability used across the organization • Own the end-to-end lifecycle of AI product capabilities, from concept and experimentation through release, adoption, and continuous improvement • Define product requirements and collaborate closely with engineering teams to develop AI-powered features and platform capabilities • Work hands-on with LLM-powered systems, including retrieval augmented generation (RAG), prompt/system design, model evaluation, and AI feature experimentation • Apply MVP and iterative product development practices to rapidly test, validate, and scale AI capabilities that deliver measurable business value • Identify opportunities to expand AI capabilities across internal workflows, enterprise knowledge systems, and business processes • Ensure AI capabilities are designed for reuse, scalability, and long-term platform value, avoiding duplication across teams • Drive alignment across AI initiatives to maintain portfolio-level visibility, investment discipline, and roadmap coherence • Partner with AI engineering and operations teams to ensure platform reliability, performance monitoring, and operational readiness • Define success metrics and monitor adoption, usage, and business impact of AI capabilities across the platform • Gather user feedback and usage insights to continuously improve platform capabilities and user experience